Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms.

Bioinformatics venn

• Bioinformatics is an interdiciplinary field of biology, computer science, and statistics.
• Bioinformatics using high through-put sequencing methods to study a large number of molecules in a biological specimen.

• The start point of a high through-put experiment is usually raw sequencing data.
• fastq files for microbiome, metagenomics, RNA seq, etc
• mzML/mzXML MS files for proteomics (and maybe metabolomics)

• Should be standardized, clear and reproducible.
• High through-put experiments data processing is the assay- or experiment-dependent, but not study dependent. The data processing precedure doesn't change with the study design.
• Proteomics: X!Tandem
• Microbiome (16 amplicon): fastq-multx, cutadapt/trimmomatic/fastx-toolkit, DADA2
• After we generate the tabular data from data processing, the data analysis step is study dependent. Different statistic model and visualization approaches can be used base on the study design.

• Principle of tidy data
• Reproducibility.
• Why R.
• Version Control (git)
• Data structure of hight through-put experiment data

1. Each measured variable should be in one column.
2. Each different observation of that variable should be in a different row.
3. There should be one table for each "kind" of variable.
4. If you have multiple tables, they should include a column in the table that allows them to be linked.

1. Each measured variable should be in one column.
2. Each different observation of that variable should be in a different row.

lipidome-messy

• The raw data should be stored well and every analysis step should be documented.

• Reproducibility allows you to be validate you analysis, and collaborate with other people.

• Using programming language is the best way to make your analysis reproducible.

  • SAS
  • R
  • Python
  • Julia

• R is a programming language designed for statistics and data analysis. R's native packages can support almost all basic statistics test (linear model, t test, correlation, etc).
• R has tremendous amount of additional packages on CRAN that extends the functionality of R for almost all fields (ecoloty, chemistry, engineering, etc.)
• The bioconductor project has also a lot of packages designated for bioinformatics, contributed by scientists all over the world.
• Very popular in academic community.
• Almost all R packages have very detailed documentations
• Ability to generate scientific report/presentations in different forms, including html, pdf, slides, and word docx.